pankajrajdeo/UMLS-Pubmed-ST-TCE-Epoch-1-QA_10K-BioASQ-PQA

SentenceTransformer based on pankajrajdeo/UMLS-Pubmed-ST-TCE-Epoch-1-QA_10K-BioASQ

This is a sentence-transformers model finetuned from pankajrajdeo/UMLS-Pubmed-ST-TCE-Epoch-1-QA_10K-BioASQ. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: pankajrajdeo/UMLS-Pubmed-ST-TCE-Epoch-1-QA_10K-BioASQ
• Maximum Sequence Length: 1024 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("pankajrajdeo/UMLS-Pubmed-ST-TCE-Epoch-1-QA_10K-BioASQ-PQA")
# Run inference
sentences = [
    'Are circulating microparticles elevated in carriers of factor V Leiden?',
    'This is the first study on circulating MP levels in subjects who are heterozygote for factor V Leiden. We report that circulating platelet and leukocyte MP are elevated in carriers of this mutation and may be important contributors to risk of thrombosis.',
    'Isovolemic hemodilution (approximately 5% hematocrit) with albumin, pentastarch, or hetastarch solutions does not result in significant hepatic ischemia or injury assessed by histology.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

Unnamed Dataset

• Size: 246,166 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 6 tokens mean: 21.28 tokens max: 48 tokens	min: 9 tokens mean: 50.22 tokens max: 241 tokens

• Samples:

  anchor	positive
  Survival of women with gestational trophoblastic neoplasia and liver metastases: is it improving?	The prognosis of patients with liver metastases from GTN has improved. Outcome may be best in those patients presenting within 2.8 years of the causative pregnancy and without very large volumes of disease.
  Do serum nitrites predict the response to prostaglandin-induced delivery at term?	A reduced level of NOx is associated with a prompt clinical response to PGE-induced labor. Provided we do not know the origin of NOx in the general circulation, these data indicate NOx levels as predictors of the response to PGE-induced delivery at term and support the hypothesis that labor onset is modulated by the endogenous NO activity.
  Is sleep deprivation an additional stress for parents staying in hospital?	Parental sleep deprivation needs to be acknowledged and accommodated when nurses and parents negotiate the care of children in hospital.

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 27,352 evaluation samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 7 tokens mean: 21.61 tokens max: 63 tokens	min: 6 tokens mean: 48.56 tokens max: 223 tokens

• Samples:

  anchor	positive
  Is dEAD-box protein p68 regulated by β-catenin/transcription factor 4 to maintain a positive feedback loop in control of breast cancer progression?	Our findings indicate that Wnt/β-catenin signaling plays an important role in breast cancer progression through p68 upregulation.
  Are obstetric medical emergency teams a step forward in maternal safety?	In the literature, there is a lack of reporting and probably of implementation of Obstetrics METs. Therefore, there is a need for more standardized experiences and reports on the implementation of various types of Obstetrics METs. We propose here a design for Obstetrics METs to be implemented in developing countries, aiming to reduce maternal mortality and morbidity resulting from obstetric hemorrhage.
  Is monocyte-Induced Prostate Cancer Cell Invasion Mediated by Chemokine ligand 2 and Nuclear Factor-κB Activity?	Co-cultures with monocyte-lineage cell lines stimulated increased prostate cancer cell invasion through increased CCL2 expression and increased prostate cancer cell NF-κB activity. CCL2 and NF-κB may be useful therapeutic targets to interfere with inflammation-induced prostate cancer invasion.

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• gradient_accumulation_steps: 4
• learning_rate: 2e-05
• weight_decay: 0.01
• num_train_epochs: 1
• warmup_ratio: 0.1
• fp16: True
• load_best_model_at_end: True
• push_to_hub: True
• resume_from_checkpoint: True

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: epoch
• prediction_loss_only: True
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 4
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 2e-05
• weight_decay: 0.01
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 1
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: False
• fp16: True
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: True
• ignore_data_skip: False
• fsdp: []
• fsdp_min_num_params: 0
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• fsdp_transformer_layer_cls_to_wrap: None
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• use_legacy_prediction_loop: False
• push_to_hub: True
• resume_from_checkpoint: True
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: False
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• include_for_metrics: []
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• dispatch_batches: None
• split_batches: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss
0.0260	100	0.0201	-
0.0520	200	0.0162	-
0.0780	300	0.0118	-
0.1040	400	0.0112	-
0.1300	500	0.0102	-
0.1560	600	0.0101	-
0.1820	700	0.0121	-
0.2080	800	0.0127	-
0.2340	900	0.008	-
0.2600	1000	0.0086	-
0.2860	1100	0.0073	-
0.3120	1200	0.0113	-
0.3380	1300	0.0084	-
0.3640	1400	0.0079	-
0.3900	1500	0.0073	-
0.4160	1600	0.0048	-
0.4420	1700	0.0088	-
0.4680	1800	0.0077	-
0.4940	1900	0.0076	-
0.5200	2000	0.0064	-
0.5460	2100	0.0074	-
0.5719	2200	0.0079	-
0.5979	2300	0.0079	-
0.6239	2400	0.0078	-
0.6499	2500	0.0073	-
0.6759	2600	0.0076	-
0.7019	2700	0.0085	-
0.7279	2800	0.0081	-
0.7539	2900	0.0083	-
0.7799	3000	0.0055	-
0.8059	3100	0.0068	-
0.8319	3200	0.007	-
0.8579	3300	0.0087	-
0.8839	3400	0.007	-
0.9099	3500	0.0068	-
0.9359	3600	0.0069	-
0.9619	3700	0.0109	-
0.9879	3800	0.0053	-
0.9999	3846	-	0.0062

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.3.1
• Transformers: 4.46.2
• PyTorch: 2.5.1+cu121
• Accelerate: 1.1.1
• Datasets: 3.1.0
• Tokenizers: 0.20.3

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}